Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters

Fatigue crack growth analysis using extended finite element method (XFEM) is an efficient way to predict the residual life of structures; however, when the structure parameters vary stochastically, it will be very hard to make accurate predictions. To bridge this research gap, this work proposed a d...

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Bibliographic Details

Published in:	Computer methods in applied mechanics and engineering Vol. 372; p. 113373
Main Authors:	Feng, S.Z., Han, X., Ma, Z.J., Królczyk, Grzegorz, Li, Z.X.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2020
Subjects:	Data-driven learning Fatigue crack propagation Stochastic model parameters XFEM Data-driven learning Fatigue crack propagation Stochastic model parameters XFEM
ISSN:	0045-7825, 1879-2138
Online Access:	Get full text
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